A Mixed Bayesian Optimization Algorithm with Variance Adaptation

Autor:	Stefan Kern, Nikolaus Hansen, Jiri Ocenasek, Petros Koumoutsakos
Rok vydání:	2004
Předmět:	Mathematical optimization Estimation of distribution algorithm Adaptive algorithm Covariance matrix Evolutionary algorithm Bayesian network CMA-ES Evolution strategy Rastrigin function Mathematics
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540230922 PPSN
DOI:	10.1007/978-3-540-30217-9_36
Popis:	This paper presents a hybrid evolutionary optimization strategy combining the Mixed Bayesian Optimization Algorithm (MBOA) with variance adaptation as implemented in Evolution Strategies. This new approach is intended to circumvent some of the deficiences of MBOA with unimodal functions and to enhance its adaptivity. The Adaptive MBOA algorithm – AMBOA – is compared with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The comparison shows that, in continuous domains, AMBOA is more efficient than the original MBOA algorithm and its performance on separable unimodal functions is comparable to that of CMA-ES.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f65a7b38d7207d3f0024273e9f90f8f5 https://doi.org/10.1007/978-3-540-30217-9_36 Zobrazit plný text záznamu